Scalable space frequency adaptive processing (SFAP)

ABSTRACT

Embodiments of the inventive concepts disclosed herein are directed to systems and methods for signal processing. An antenna array can receive signal information. A signal processing device can perform a time-domain-to-transform-domain transform with N transform domain bins, on the signal information. The signal processing device can determine N covariance matrices each corresponding to a respective one of the N transform domain bins. The signal processing device can group covariance matrices from the N covariance matrices into groups of M covariance matrices. Each group can correspond to a respective group of M adjacent bins from the N bins. The signal processing device can produce a combined spatial covariance matrix for each group of M covariance matrices, by performing a weighted combination of covariance matrices within the respective group of M covariance matrices. The signal processing device can calculate spatial weights from each of the spatial covariance matrices, for anti-jamming processing.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/940,944 filed on Mar. 29, 2018, and entitled “SCALABLE SPACEFREQUENCY ADAPTIVE PROCESSING (SFAP)”, which is incorporated herein byreference in its entirety for all purposes.

BACKGROUND

Many systems used in communications, surveillance or militaryapplications for instance, use information received via communicationssignals or satellite signals. Frequently, such systems (e.g., globalnavigation satellite system (GNSS) receivers) may operate in thepresence of interfering or jamming signals. To improve the reception andprocessing of such signals, anti-jamming capabilities are becomingincreasingly useful or necessary to suppress or remove jammers orinterference. However, anti-jamming systems are typicallycustom-designed to address a particular performance requirement, andcannot readily modify its level of anti-jamming processing. There arechallenges to addressing this limitation efficiently andcost-effectively.

SUMMARY

In one aspect, embodiments of the inventive concepts disclosed hereinare directed to a system for signal processing. The system can includean antenna array to receive signal information. A signal processingdevice can include one or more processors. The signal processing devicecan perform a time domain to transform domain transform (or TTT) with Ntransform domain bins (e.g., frequency bins), on the signal information.Examples of TTT can include Fourier transform such as fast Fouriertransform (FFT), and wavelet transform. The signal processing device candetermine N covariance matrices each corresponding to a respective oneof the N transform domain bins (e.g., frequency bins). The signalprocessing device can group covariance matrices from the N covariancematrices into groups of M covariance matrices. Each group can correspondto a respective group of M adjacent transform domain bins from the Ntransform domain bins. The signal processing device can produce acombined spatial covariance matrix for each group of M covariancematrices, by performing a weighted combination of covariance matriceswithin the respective group of M covariance matrices. The signalprocessing device can calculate spatial weights from each of the spatialcovariance matrices, for anti-jamming or interference mitigationprocessing to control the antenna array.

In some embodiments, and as an example, the signal processing device canperform a time domain to frequency domain transform (e.g., FFT) with Nfrequency bins, on the signal information. The signal processing devicecan determine N covariance matrices each corresponding to a respectiveone of the N frequency bins. The signal processing device can groupcovariance matrices from the N covariance matrices into groups of Mcovariance matrices. Each group can correspond to a respective group ofM adjacent frequency bins from the N frequency bins. The signalprocessing device can produce a combined spatial covariance matrix foreach group of M covariance matrices, by performing a weightedcombination of covariance matrices within the respective group of Mcovariance matrices. The signal processing device can calculate spatialweights from each of the spatial covariance matrices, for anti-jammingor interference mitigation processing to control the antenna array.

In some embodiments, the antenna array can receive the signalinformation in a radio frequency (RF) signal. The system can furtherinclude an analog to digital converter (ADC) configured to convert ananalog signal from RF circuitry coupled to the antenna array, into adigital signal. The signal processing device can perform the TTT on thedigital signal. The antenna array can receive the signal information ina GNSS signal.

In some embodiments, the signal processing device can control theantenna array using a first value for M at a first time instance, and asecond value for M at a second time instance. The signal processingdevice can set or receive a value for M in response to a level ofinterference detected in signals received via the antenna array. Forinstance, the value of M can be user controlled or automatically set inresponse to the type and level of interference detected in the signals.The system can further include an excision component configured tofilter a result of the anti-jamming or interference mitigationprocessing, at a resolution matched to the N bins. The signal processingdevice can perform an inverse FFT (IFFT) with N bins. The antennaelements of the antenna array can be steered according to theanti-jamming or interference mitigation processing. The antenna elementsof the antenna array can be controlled or steered using the calculatedspatial weights.

In a further aspect, embodiments of the inventive concepts disclosedherein are directed to a method for interference mitigation. The methodcan include receiving signal information via an antenna array. A signalprocessor can perform a time domain to transform domain transform (TTT)with N transform domain bins (e.g., frequency bins, where the transformdomain is a frequency domain), on the signal information. The signalprocessor can determine N covariance matrices each corresponding to arespective one of the N transform domain bins. The signal processor cangroup covariance matrices from the N covariance matrices into groups ofM covariance matrices. Each group corresponding to a respective group ofM adjacent transform domain bins from the N transform domain bins. Thesignal processor can produce a combined spatial covariance matrix foreach group of M covariance matrices, by performing a weightedcombination of covariance matrices within the respective group of Mcovariance matrices. The signal processor can calculate spatial weightsfrom each of the spatial covariance matrices, for anti-jamming orinterference mitigation processing, to control the antenna array.

In some embodiments, and as an example the signal processor can performa time domain to frequency domain transform (such as FFT) with Nfrequency bins, on the signal information. The signal processor candetermine N covariance matrices each corresponding to a respective oneof the N frequency bins. The signal processor can group covariancematrices from the N covariance matrices into groups of M covariancematrices. Each group corresponding to a respective group of M adjacentfrequency bins from the N frequency bins. The signal processor canproduce a combined spatial covariance matrix for each group of Mcovariance matrices, by performing a weighted combination of covariancematrices within the respective group of M covariance matrices. Thesignal processor can calculate spatial weights from each of the spatialcovariance matrices, for anti-jamming or interference mitigationprocessing, to control the antenna array.

In some embodiments, the antenna array can receive the signalinformation in a radio frequency (RF) signal. An analog to digitalconverter (ADC) can convert an analog signal from RF circuitry coupledto the antenna array, into a digital signal. The signal processingdevice can perform the TTT on the digital signal. The antenna array canreceive the signal information in a global navigation satellite system(GNSS) signal. The signal processing device can control the antennaarray using a first value for M at a first time instance, and a secondvalue for M at a second time instance. The value for M can be set inresponse to a level of interference detected in signals received via theantenna array. An excision component can filter a result of the anti-jamming or interference mitigation processing, at a resolution matched tothe N bins. The signal processing device can perform an inverse TTT(ITTT) with N bins. The antenna elements of the antenna array can besteered according to the anti-jamming or interference mitigationprocessing. The antenna elements of the antenna array can be steeredaccording to the calculated spatial weights.

In yet another aspect, embodiments of the inventive concepts disclosedherein are directed to a global navigation satellite system (GNSS)device. The GNSS device can include an antenna array that can receiveGNSS signal information. A signal processing device can include one ormore processors. The signal processing device can perform a fast Fouriertransform (FFT) with N frequency bins, on the GNSS signal information.The signal processing device can determine N covariance matrices eachcorresponding to a respective one of the N frequency bins. The signalprocessing device can group covariance matrices from the N covariancematrices into groups of M covariance matrices, each group correspondingto a respective group of M adjacent frequency bins from the N frequencybins. The signal processing device can produce a combined spatialcovariance matrix for each group of M covariance matrices, by performinga weighted combination of covariance matrices within the respectivegroup of M covariance matrices. The signal processing device cancalculate spatial weights from each of the spatial covariance matrices,for anti-jamming or interference mitigation processing to control theantenna array.

In some embodiments, the signal processing device can control theantenna array using a first value for M at a first time instance, and asecond value for M at a second time instance. The signal processingdevice can set or receive a value for M in response to a level ofinterference detected in signals received via the antenna array. Thesignal processing device can perform an inverse FFT (IFFT) with N bins.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of the inventive concepts disclosed herein may be betterunderstood when consideration is given to the following detaileddescription thereof. Such description makes reference to the includeddrawings, which are not necessarily to scale, and in which some featuresmay be exaggerated and some features may be omitted or may berepresented schematically in the interest of clarity. Like referencenumerals in the drawings may represent and refer to the same or similarelement, feature, or function. In the drawings:

FIG. 1 is a block diagram of an example embodiment of a system forsignal processing in accordance with some embodiments of the inventiveconcepts disclosed herein; and

FIG. 2 shows a flow diagram of an example embodiment of a method forsignal processing in accordance with some embodiments of the inventiveconcepts disclosed herein.

DETAILED DESCRIPTION

Before describing in detail embodiments of the inventive conceptsdisclosed herein, it should be observed that the inventive conceptsdisclosed herein include, but are not limited to a novel structuralcombination of components and circuits, and not to the particulardetailed configurations thereof. Accordingly, the structure, methods,functions, control and arrangement of components and circuits have, forthe most part, been illustrated in the drawings by readilyunderstandable block representations and schematic diagrams, in ordernot to obscure the disclosure with structural details which will bereadily apparent to those skilled in the art, having the benefit of thedescription herein. Further, the inventive concepts disclosed herein arenot limited to the particular embodiments depicted in the schematicdiagrams, but should be construed in accordance with the language in theclaims.

In some aspects, embodiments of the inventive concepts disclosed hereinare directed to systems and methods for signal processing that caninclude and extend on some aspects of space frequency adaptiveprocessing (SFAP). SFAP can be used as an anti jam (AJ) technique thatcan be applied on any communications signals, such as GNSS based signalswhich can include GPS, GLONASS, Galileo, Beidou and other regionalsystems' satellite signals. AJ systems are typically implemented aspoint designs for mitigating specific threat scenarios that may beunique to each market (e.g., geographical region, or technologyinvolved). SFAP is a technique that uses the Fast Fourier Transform(FFT) to subdivide a frequency band of interest into sub-bands. Adaptivespatial processing is then performed in each sub-band prior tore-combining with an inverse FFT. For SFAP, a high-end design mightimplement 256 FFT bins for instance, whereas a low-end design mightimplement 32 bins for example. While based on the same underlying FFTprocessing, these two example designs would typically be two distinctsets of software and firmware, to offer two different but fixed levelsof performance. Instead of achieving different performance usingdistinct designs, embodiments of the present inventive concepts providefor systems and methods that allow for adaptable spatial processing forinterference mitigation. For instance, the processing can be adapted tobe less aggressive when the threat environment can be mitigated withless capable processing.

AJ approaches such as Spatial-only Adaptive Processing (SAP) orSpace-Time Adaptive Processing (STAP) can provide a single covariancematrix that can be used to generate situational awareness informationsuch as that associated with direction finding. The ability to performspatial-only adaptive processing or to generate a single spatialcovariance matrix to support direction finding is not available inconventional SFAP as the processing is done in the frequency domain.Spatial-only covariance generation and adaptive processing is availablein accordance with some aspects of the inventive concepts disclosedherein, by setting M equal to N. In accordance with some aspects of theinventive concepts disclosed herein, the present systems and methods canuse a time-domain to transform/other-domain transform (sometimesreferred to herein as a TT transform or a TTT) with a number of binssubdivided from a transform domain band/range (e.g., frequency band) ofinterest, and can use one or more spatial covariance matrices to supportsituational awareness. A TTT can refer to a transformation from a timedomain to another domain (sometimes generally referred to as a transformdomain). Taking FFT as an example of TTT, FFT describes a transformationfrom time domain to frequency domain. Taking wavelet transform as oneexample of TTT, wavelet transform describes a transformation from timedomain to a “scale” domain which is related to frequency, and/or toanother time domain. TTT can also refer to a transformation from onetime domain to another time domain. Accordingly, a transform domain canrefer to any type of domain, such as frequency domain, wavelet “scale”domain, a certain time domain, and so on.

Referring to FIG. 1, one example embodiment of a system for signalprocessing is depicted. The system can include a signal processor 100,an antenna array 102 comprising one or more antenna elements, and/orradio frequency (RE) front end components 104 configured for receivingincoming RF signals. The incoming RE signals may be converted to digitalsignals through analog to digital converters (ADC) 106, and are thenconverted from a serial stream of data to a block of data inserial-to-parallel converters 108, for signal processing. The signalprocessor (sometimes referred to as a digital signal processor) 100 caninclude time-domain to transform-domain transform (ITT) engines 110,that can carry out a ITT such as a fast Fourier transform (FFT), inwhich N points are taken resulting in N frequency bins (or transformdomain bins). A spatial processor 112 can establish covariance matricesfor each of the N bins, and can establish a number of spatial covariancematrices, each obtained by performing a weighted combination ofcovariance matrices within a corresponding group of M covariancematrices. The spatial processor 112 can calculate spatial weightscorresponding to each of the spatial covariance matrices. The spatialweights can be used to electronically and/or adaptively steer theantenna elements. The spatial weights can be used for anti-jamming orinterference mitigation processing in the spatial processor 112, forexample by combining with data from each of the antenna elements toperform spatial processing. Excision or filtering is optionally appliedby an excision component 118 on an output of the spatial processor 112,after the spatial processing. An inverse TTT (ITTT) engine 114 canperform inverse TTT (e.g., inverse FFT) on an output of the spatialprocessor 112 or the excision component 118, to produce time domain data(e.g., GNSS based data such as C/A code, Y-code and/or M-code, orcommunications data). A receiver 116, such as a GNSS receiver or acommunications receiver, can receive the time domain data for processing(e.g., to calculate positioning, velocity or timing (PVT) information,or to decode communications content).

The system can be part of an AJ system (e.g., an anti jam antenna system(AJAS)), a GNSS-based system or a communications system. The system canbe hosted or installed on a vehicle (e.g., any land-based, water-based,airborne or space vehicle, or an electric or autonomous vehicle), asatellite, a robotic entity, a building (e.g., station, communicationscenter, command center), or on any operations platform (e.g., mobileoperations center or module). Although portions of this disclosure maydescribe using the present systems and methods in the context ofanti-jamming, this is merely by way of illustration and not intended tobe limiting in any way. Embodiments of the present systems and methodscan be used for any type of noise or interference mitigation.

Each of the above-mentioned elements or entities (and others disclosedherein) is implemented in hardware, or a combination of hardware andsoftware. For instance, some of these elements or entities can includeany application, program, library, script, task, service, process or anytype and form of executable instructions executing on hardware of thesystem. The hardware includes circuitry such as one or more processors,memory devices, connections or bus structures, and/or communicationinterfaces, in one or more embodiments.

The antenna array 102 can include one or more antenna elements, whichcan be steerable. By way of illustration, two antenna elements are shownin FIG. 1 for simplicity. Each antenna element is coupled to its ownreceive chain along which a corresponding received signal is processed(e.g., in parallel with the processing of other received signals in theother receive chains). The antenna array 102 can be part of an AJAS. Theantenna array 102 can receive any type of satellite or positioningsignals, such as GNSS based signals which can include GPS, GLONASS,Galileo, Beidou and other regional systems' signals. A GNSS based signalcan include a public, civilian, clear or open signal (e.g., a C/A codesignal) and/or an encrypted signal (e.g., an M-code or Y-code signal,for military use for instance).

The antenna array can receive any type of communications signals usingany communications protocol, such as one under wireless local areanetwork (WLAN) or wireless fidelity (Wifi, e.g., any variant of IEEE802.11 including 802.11a/b/g/n), wireless personal area network (WPAN,e.g., Bluetooth, Zigbee, ultra-wideband (UWB), WiMedia, Wibree, wirelessuniversal serial bus, ONE-NET, etc.), cellular (e.g., CDMA/CDMA2000,GSM/UMTS, UMTS over W-CDMA, UMTS-TDD, LTE, 3G/4G/5G, and so on),wireless metropolitan area network WIMAN (e.g., WiMax), and other widearea network, WAN technologies (e.g., iBurst, Flash-OFDM, EV-DO, HSPA,RTT, EDGE, GPRS), LoRa, infrared (IR), radio frequency identification(RFID), Ethernet, optical (e.g., fiber, lightwave transmission, laser),ultrasonic, dedicated short range communications (DSRC), near fieldcommunication (NFC), radar, V2X, though not limited to these.

The antenna array 102 can be electronically steered. The antenna array102 can operate with anti-jamming and/or interference removalmechanisms. For example, the spatial processor 112 can calculate spatialweights to steer the antenna array's antenna elements to null or removejamming signals. The spatial weights can represent a solution formaximizing SINR (and/or satisfying other constraints, such as nulling acertain jammer, or limiting measurement errors below a threshold) forthe antenna array 102. The system can use signal processing techniquessuch as space-frequency adaptive processing using FFTs for instance, tofilter jamming or other interference signals from communications orpositioning signals. The antenna array 102 can be controlled orconfigured to null, reduce or remove jamming and/or interference signalsin a received GNSS signal for instance. The antenna array 102 canperform beamforming with its antenna elements to increase or improvereception of a desired signal.

Each antenna element of the antenna array 102 can receive an RF signal,which is transmitted to the RF front end component 106 in a receivechain. The incoming RF signal can be converted to digital signalsthrough the ADC 106 in the receive chain, and are then converted from aserial stream of data to a block of data in a serial-to-parallelconverter 108, for signal processing.

The signal processor 100 can include hardware such as circuitry andprocessing elements, as well as software such as TTT algorithms and/orspatial processing algorithms. The signal processor 100 can include TTTengines 110 (such as a FFT, wavelet or other time-domain to transformdomain transform engine) for each receive chain, that can carry out TTT(e.g., FFT, wavelet or other TT transform). TTTs, such as FFTs, can beused for processing observed or received signals. Weighting functions,referred to as windows, can be applied to data in a received signalbefore TTT to reduce spectral leakage associated with the finiteobservation intervals. Windowing can achieve the effect of attenuatingcontribution from bins located several bins and beyond away from acurrent bin.

By way of an example, in applying a SFAP algorithm, an N point FFT ofinput data can be taken by the signal processor 100, resulting in Nfrequency bins. In each frequency bin, a set of weights can becalculated to remove the jamming power in a given frequency bin. In someembodiments, the TTT engine 110 of the signal processor 100 can performa TTT where N points are taken resulting in N transform domain bins.When the TTT corresponds to wavelet transform, the transform domain binscan corresponds to bins in the scale domain and/or time domain (sinceWavelet is a 2 dimensional transform to a scale domain and a timedomain). The value of N may be determined or fixed based on a specificperformance requirement, to address an expected type and/or level ofjamming or interference. For a larger value of N, a longer time windowis typically used to sample the signals, leading to more data forcomputational processing. For example, an AJ system may be implementedwith 256, 512, 1024 or any other number of transform domain bins orsub-bands. In accordance with some aspects of the inventive conceptsdisclosed herein, the number of bins for TTT (and the correspondinghardware and/or software) can be designed for a defined number of bins(N), but the level of spatial processing can be adaptively adjustedusing the underlying N bins in groups of M for instance. Hence, thesystem can implement a SFAP design that uses the highest number of bins(N) anticipated to mitigate a jamming threat space, and adjust spatialprocessing accordingly for adequate performance as the interferencescenario changes.

The TTT engine 110 can produce data for each antenna element (orchannel) of the antenna array 102. The spatial processor 112 can use thedata corresponding to each antenna element to form a covariance matrixfor each of the N bins. For instance, a covariance matrix of size K×Kcan be established for an antenna array 102 with K antenna elements. Thecovariance matrix can be indicative of a spatial orientation of jammingsignal(s) relative to the antenna elements. The spatial processor 112can establish covariance matrices for each of the N bins. The spatialprocessor 112 can establish N covariance matrices (sometimes referred toas covariances), one for each of the N bins. The spatial processor 112can include a function for combining groups of covariances. The spatialprocessor 112 can perform a weighted combination of the covariancematrices into N/M spatial covariance matrices for instance. The spatialprocessor 112 can combine or sum groups of covariances. This can be oneway to effectively change the number of bins used for spatial processingand provides a means to vary the resolution of spatial processingwithout changing the underlying TTT structure (of N bins).

The function or method for combining covariances can include a weightedcombination of covariances. A weighted combination of covariances caninclude matrix addition, or entrywise sum, as shown below for example:

$\begin{matrix}{{A + B} = {\begin{bmatrix}a_{11} & a_{12} & \cdots & a_{1\; n} \\a_{21} & a_{22} & \cdots & a_{2\; n} \\\vdots & \vdots & \ddots & \vdots \\a_{m\; 1} & a_{m\; 2} & \cdots & a_{m\; n}\end{bmatrix} + \begin{bmatrix}b_{11} & b_{12} & \cdots & b_{1\; n} \\b_{21} & b_{22} & \cdots & b_{2\; n} \\\vdots & \vdots & \ddots & \vdots \\b_{m\; 1} & b_{m\; 2} & \cdots & b_{m\; n}\end{bmatrix}}} \\{= \begin{bmatrix}{a_{11} + b_{11}} & {a_{12} + b_{12}} & \cdots & {a_{1\; n} + b_{1\; n}} \\{a_{21} + b_{21}} & {a_{22} + b_{22}} & \cdots & {a_{2\; n} + b_{2\; n}} \\\vdots & \vdots & \ddots & \vdots \\{a_{m\; 1} + b_{m\; 1}} & {a_{m\; 2} + b_{m\; 2}} & \cdots & {a_{mn} + b_{mn}}\end{bmatrix}}\end{matrix}$A weighted combination of covariances can include multiplying a weightto each matrix prior to addition, for example:

${{k\begin{bmatrix}a & b \\c & d\end{bmatrix}} + {l\begin{bmatrix}A & B \\C & D\end{bmatrix}}} = \begin{bmatrix}{{ka} + {lA}} & {{kb} + {lB}} \\{{kc} + {lC}} & {{kd} + {lD}}\end{bmatrix}$where k and l are the applied weights.

Each group of matrices to be subject to weighted combination can includeany number of matrices selected from the N covariance matrices. Forexample, M can be any integer that is 2 or larger. M can be such that Ncan be divisible by M to yield an integer. Each group of matrices can bematrices of M adjacent bins, or of M bins that are proximate to eachother. In some embodiments, instead of a fixed number M, the groups ofmatrices can have variable group sizes, for instance ranging from 1 to 5(or some other number of) matrices. As an example, the groupings cancorrespond to adjacent bins grouped as follows: first 2 bins in group 1,next 3 bins in group 2, next 1 bin in group 3, next 3 bins in group 4,and so on. Adjacent bins can be grouped to take advantage of the factthat data in adjacent bins are more closely related, and/or thatadjacent bins are correlated due to windowing. Other types of groupingcan be performed as well, e.g., first M even-numbered bins, first Modd-number bin, next M even-numbered bins, and so on. In someimplementations, N does not have to be exactly divisible by M. Forinstance, one or more bins can be included in at least two differentgroups of covariances. In another example, at least one group ofcovariances may have less than or more than M covariances.

For example, the spatial processor 112 can perform weighted combinationof adjacent groups of 2 covariances in a 256 bin design to provideperformance comparable to 128 bin SFAP. The performance can actually bebetter than 128 bins SFAP due to the characteristics of the 256 bin TTT(e.g., FFT) window.

The amount of weighted combination or summation (e.g., the value of M)or the amount of spatial processing can be controllable, and can beupdated or adjusted in real-time. Real-time control of the number ofbins (or number of spatial covariance matrices) for spatial processingcan be achieved by monitoring the performance of the system inreal-time. The spatial processor 112 can be updated to for instance touse a fewest number of bins for which an expected or defined minimumlevel of system performance can be met. Performance metrics used to makethe determination can be unique to the type of system in which or withwhich one or more embodiments of the present systems and methods arebeing used (e.g., GNSS or communications receiver). Increasing M canlead to a lower number of computations for the spatial weights, as theoverall number of weights to be computed can be lower due to thecombination of covariances. In a complex jamming scenario which canrequire more granular spatial processing, the value of M can be adjustedto a lower value, to increase the number of bins or resolution forspatial processing.

In one illustrative case, a spatial-only covariance can be generated bysumming up the covariances from all bins. This spatial-only covariancecan be provided to direction finding algorithms such as those usingSTAP.

The spatial processor 122 can incorporate an adaptive weight solver thatcan perform calculations for the maximum number of bins (N), that canalso be throttled back when covariance weighted combination is active,in order to reduce power. The spatial processor 122 can use its adaptiveweight solver to calculate spatial weights corresponding to each of thespatial covariance matrices. The spatial weights solved for a particularcombined spatial covariance matrix can for example be applied to eachbin associated with the group of covariances from which the combinedspatial covariance matrix is formed. The spatial weights can be used foranti-jamming or interference mitigation processing in the spatialprocessor 112, to electronically configure, steer or adjust the antennaarray 102. In addition, the spatial weights can be applied to antennadata in respective ones of the N bins, and an inverse TTT (e.g., ITTT)can be performed by the ITTT engine 114 based on the N bins for example,to provide data in the time domain. The data in the time domain can beused or processed by a receiver 116. For example, the receiver 116 caninclude a GNSS receiver, and the data in the time domain can includeGNSS data such as C/A code, Y-code and/or M-code data. The GNSS receivercan process the data to generate PNT or PVT information for example. Asanother example, the receiver 116 can include a communications receiver,and the data in the time domain can include satellite communications orother communications data. The GNSS receiver can process and/or decodethe data to output communications content (e.g., satellite phone audiotransmission) to a user for example.

In some embodiments, the system can incorporate a higher resolutionsecond stage (e.g., excision or filtering stage) in the SFAP processing,provided by the excision component 118 for instance. For example, thespatial processor 122 can baseline with an N (e.g., 1024) bin design toenable the second stage to match the filtering resolution (e.g., 1024bins) in an excision process exactly, without requiring spatial weightsolving in each of the N bins in the first stage of spatial processing(e.g., spatial weights can be solved at less than N bins) at the spatialprocessor 112. As such, embodiments of the present systems and methodscan support flexible architectures in SFAP where the stage 1 and stage 2processing can have differing numbers of bins.

Referring now to FIG. 2, one embodiment of a method for managing globalnavigation satellite system (GNSS) receivers is depicted. The methodincludes receiving signal information via an antenna array (200). Asignal processor can perform a time domain to transform domain transformwith N transform domain bins, on the signal information (202). Thesignal processor can determine N covariance matrices each correspondingto a respective one of the N transform domain bins (204). The signalprocessor can group covariance matrices from the N covariance matricesinto groups of M covariance matrices, each group corresponding to arespective group of M adjacent transform domain bins from the Ntransform domain bins (206). The signal processor can produce a combinedspatial covariance matrix for each group of M covariance matrices, byperforming a weighted combination of covariance matrices within therespective group of M covariance matrices (208). The signal processorcan calculate spatial weights from each of the spatial covariancematrices, for anti-jamming or interference mitigation processing, tocontrol or steer the antenna array (210).

Referring now to step 200, and in some embodiments, an antenna array canreceive signal information. For example, the antenna array can receivethe signal information in a positioning signal such as a globalnavigation satellite system (GNSS) signal, or in a communicationssignal. The antenna array 102 can receive any type of satellite orpositioning signals, such as GNSS based signals which can include GPS,GLONASS, Galileo, Beidou and other regional systems' signals. Theantenna array can receive any type of communications signals using anycommunications protocol. The antenna array can receive the signalinformation in one or more radio frequency (RF) signals. Each antennaelement of the antenna array can receive an incoming RF signal. RFcircuitry coupled to the corresponding antenna element, such as a RFfront end component, can receive and process the incoming RF signal. Ananalog to digital converter (ADC) can convert an analog signal from theRF circuitry into a digital signal, which is then converted from aserial stream of data to a block of data in a serial-to-parallelconverter 108, for signal processing. Each antenna element can becoupled to its own receive chain, along which a corresponding receivedsignal is processed (e.g., in parallel with the processing of otherreceived signals in the other receive chains).

Referring now to step 202, and in some embodiments, a signal processorcan perform a time domain to transform domain transform (TTT) with Ntransform domain bins, on the signal information. The signal processor100 can perform the TTT (e.g., a FFT or wavelet transform) on thedigital signal. The signal processor 100 can include TTT engines 110 foreach receive chain, that can each carry out a TTT such as a fast Fouriertransform (FFT), in which N points are taken resulting in N frequencybins. Weighting functions, referred to as windows, can be applied todata in a received signal before TTT to reduce spectral leakageassociated with the finite observation intervals. Each TTT engine 110 ofthe signal processor 100 can perform a TTT where N points are takenresulting in N transform domain bins. The value of N may be determinedor fixed based on a specific performance requirement, to addressexpected types and/or levels of jamming or interference.

Referring now to step 204, and in some embodiments, the signal processorcan determine N covariance matrices each corresponding to a respectiveone of the N transform domain bins. The spatial processor 112 canestablish covariance matrices for each of the N bins. The signalprocessor can include a spatial processor 112 that can establishcovariance matrices for each of the N bins. The TTT engine 110 canproduce data for each antenna element (or channel) of the antenna array102. The spatial processor 112 can use the data corresponding to eachantenna element to form a covariance matrix for each of the N bins. Forinstance, a covariance matrix of size K×K can be established for anantenna array 102 with K antenna elements. The covariance matrix can beindicative of a spatial orientation of jamming signal(s) relative to theantenna elements.

Referring now to step 206, and in some embodiments, the signal processorcan group covariance matrices from the N covariance matrices into groupsof M covariance matrices. Each group can correspond to a respectivegroup of M adjacent transform domain bins from the N transform domainbins. A user or an algorithm (e.g., using a feedback system) can set avalue for M to achieve a desired minimum level of system performance.

Each group of matrices can include any number of matrices selected fromthe N covariance matrices. For example, M can be any integer that is 2or larger. M can be such that N can be divisible by M to yield aninteger. Each group of matrices can be matrices of M adjacent bins, orof M bins that are proximate to each other. Instead of a single valuefor M, the signal processor can group covariance matrices from the Ncovariance matrices into groups of variable numbers of covariancematrices, in some embodiments. Adjacent bins can be grouped to takeadvantage of the fact that data in adjacent bins are more closelyrelated, and/or that adjacent bins are correlated due to windowing.Other types of grouping can be performed as well, e.g., first Meven-numbered bins, first M odd-number bin, next M even-numbered bins,and so on. In some implementations, N does not have to be exactlydivisible by M. For instance, one or more bins can be included in atleast two different groups of covariances. In another example, at leastone group of covariances may have less than or more than M covariances.

Referring now to step 208, and in some embodiments, the signal processorcan produce a combined spatial covariance matrix for each group of Mcovariance matrices, by performing a weighted combination of covariancematrices within the respective group of M covariance matrices. Thespatial processor 112 can include a function for combining groups ofcovariances. The spatial processor 112 can combine or sum groups ofcovariances. The spatial processor 112 can perform a weightedcombination of the covariance matrices into M spatial covariancematrices. The spatial processor 112 can establish a number of spatialcovariance matrices, each obtained by performing a weighted combinationof covariance matrices within a corresponding group of M (or othernumber of) covariance matrices. This can effectively change the numberof bins used for spatial processing and provides a means to vary theresolution of spatial processing without changing the underlying TTTstructure (of N bins). The function or method for combining covariancescan include a weighted combination of covariances. A weightedcombination of covariances can include matrix addition, or entrywisesum. A weighted combination of covariances can include multiplying aweight to each matrix prior to addition.

Referring now to step 210, and in some embodiments, the signal processorcan calculate spatial weights from each of the spatial covariancematrices, for anti-jamming or interference mitigation processing, tocontrol or steer the antenna array. The spatial processor 112 cancalculate spatial weights corresponding to each of the spatialcovariance matrices. The spatial processor 122 can use an adaptiveweight solver to calculate spatial weights corresponding to each of thespatial covariance matrices. The spatial weights can represent asolution for maximizing SINR (and/or satisfying a certain constraint)for the antenna array 102.

The antenna elements of the antenna array can be digitally steeredaccording to the anti-jamming or interference mitigation processing(e.g., using the spatial weights). For example, the antenna array 102can be steered, controlled or configured to null, reduce or removejamming and/or interference signals in a received GNSS signal by usingthe spatial weights. The antenna array 102 can be digitally controlledor steered to perform beamforming with its antenna elements so as toincrease or improve reception of a desired signal.

The spatial weights solved for a particular combined spatial covariancematrix can be applied to transform domain (e.g., frequency domain) datacorresponding to the N bins. The spatial weights solved for a particularcombined spatial covariance matrix can for example be applied to eachbin associated with the group of covariances from which the combinedspatial covariance matrix is formed, for example, before excision and/oran inverse TTT is performed. The spatial weights can be applied to datain respective ones of the N bins, and an inverse TTT (e.g., IFFT) can beperformed by the ITTT engine 114 based on the N bins, to provide data inthe time domain to be processed or used by a GNSS receiver for instance.

In some embodiments, the system can incorporate a higher resolutionsecond stage, which corresponds to an excision or filtering stage. Anexcision component 118 can filter a result of the anti-jamming orinterference mitigation processing, for example on antenna data from thebins after the spatial weights have been applied, at a resolutionmatched to the N bins. For instance, the spatial weights can be appliedto data in the corresponding N bins, and the filtering can be performedon the data at a resolution matched to the N bins. This second stage canbe optional and can be performed prior to the inverse TTT, for example.

The signal processing device can include an ITTT engine to perform theinverse TTT (ITTT) with N bins, using the calculated spatial weights.The ITTT engine 114 can generate data in the time domain, that includesC/A code, Y-code or M-code data for example, to feed to a GNSS receiver116 to obtain positioning, navigational and timing (PNT) information forinstance. The ITTT engine can generate data in the time domain, thatincludes a communications signal in a particular communications protocolfor example, to feed to a communicates receiver 116 to decode andextract content from the communications signal for instance.

The amount of weighted combination or summation (e.g., the value of M)or the amount of spatial processing can be controllable, and can beupdated or adjusted in real-time based on monitoring the systemperformance or under control of an external user of the system. Forinstance, the signal processing device can control the antenna arrayusing a first value for M at a first time instance, and a second valuefor M at a second time instance that is later than the first timeinstance. Where the first value is lower than the second value, thisincrease can be to implement an adjustment to the spatial processing toreduce a resolution of spatial processing to a level that still meets adesired performance requirement under reduced complexity jammingconditions. Where the first value is higher than the second value, thisincrease can be to implement an adjustment to the spatial processing toincrease a resolution of the spatial processing to a level that meets adesired system performance under increased complexity jammingconditions.

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of theinventive concepts disclosed herein. The order or sequence of anyoperational flow or method operations may be varied or re-sequencedaccording to alternative embodiments. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions and arrangement of the exemplary embodimentswithout departing from the broad scope of the inventive conceptsdisclosed herein.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. Embodiments of the inventive concepts disclosed herein maybe implemented using existing computer operational flows, or by aspecial purpose computer operational flows for an appropriate system,incorporated for this or another purpose, or by a hardwired system.Embodiments within the scope of the inventive concepts disclosed hereininclude program products comprising machine-readable media for carryingor having machine-executable instructions or data structures storedthereon. Such machine-readable media can be any available media that canbe accessed by a special purpose computer or other machine with anoperational flow. By way of example, such machine-readable media cancomprise RAM, ROM, EPROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othermedium which can be used to carry or store desired program code in theform of machine-executable instructions or data structures and which canbe accessed by a general purpose or special purpose computer or othermachine with an operational flow. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or a combination of hardwired or wireless) to amachine, the machine properly views the connection as a machine-readablemedium. Thus, any such connection is properly termed a machine-readablemedium. Combinations of the above are also included within the scope ofmachine-readable media. Machine-executable instructions include, forexample, instructions and data which cause a special purpose computer,or special purpose operational flowing machines to perform a certainfunction or group of functions.

What is claimed is:
 1. A system for signal processing, comprising: anantenna array configured to receive signal information; and a signalprocessing device comprising one or more processors, the signalprocessing device configured to: perform a wavelet transform with Ntransform domain bins, on the signal information; determine N covariancematrices each corresponding to a respective one of the N transformdomain bins; group covariance matrices from the N covariance matricesinto groups of M covariance matrices, each group corresponding to arespective group of M adjacent transform domain bins from the Ntransform domain bins; produce a combined spatial covariance matrix foreach group of M covariance matrices, by combining covariance matriceswithin the respective group of M covariance matrices; and calculatespatial weights from each of the spatial covariance matrices, foranti-jamming or interference mitigation processing to control theantenna array.
 2. The system of claim 1, wherein the antenna array isconfigured to receive the signal information in a radio frequency (RF)signal, the system further comprising: an analog to digital converter(ADC) configured to convert an analog signal from RF circuitry coupledto the antenna array, into a digital signal, wherein the signalprocessing device is configured to perform the wavelet transform on thedigital signal.
 3. The system of claim 1, wherein the antenna array isconfigured to receive the signal information in a global navigationsatellite system (GNSS) signal.
 4. The system of claim 1, wherein thesignal processing device is configured to control the antenna arrayusing a first value for M at a first time instance, and a second valuefor M at a second time instance.
 5. The system of claim 1, wherein thesignal processing device is configured to set or receive a value for Min response to a level of interference detected in signals received viathe antenna array.
 6. The system of claim 1, wherein the signalprocessing device is configured to perform an inverse wavelet transformwith N bins.
 7. The system of claim 1, wherein antenna elements of theantenna array are steered or controlled according to the calculatedspatial weights.
 8. The system of claim 1, further comprising anexcision component configured to filter a result of the anti-jamming orinterference mitigation processing, at a resolution matched to N bins.9. A method for interference mitigation, comprising: receiving signalinformation via an antenna array; performing, by a signal processor, awavelet transform with N transform domain bins, on the signalinformation; determining, by the signal processor, N covariance matriceseach corresponding to a respective one of the N transform domain bins;grouping covariance matrices from the N covariance matrices into groupsof M covariance matrices, each group corresponding to a respective groupof M adjacent transform domain bins from the N transform domain bins;producing, by the signal processor, a combined spatial covariance matrixfor each group of M covariance matrices, by combining covariancematrices within the respective group of M covariance matrices; andcalculating, by the signal processor, spatial weights from each of thespatial covariance matrices, for anti-jamming or interference mitigationprocessing, to control the antenna array.
 10. The method of claim 9,comprising: receiving, by the antenna array, the signal information in aradio frequency (RF) signal; converting, by an analog to digitalconverter (ADC), an analog signal from RF circuitry coupled to theantenna array, into a digital signal; and performing, by the signalprocessor, the wavelet transform on the digital signal.
 11. The methodof claim 9, comprising receiving, by the antenna array, the signalinformation in a global navigation satellite system (GNSS) signal. 12.The method of claim 9, comprising controlling, by the signal processor,the antenna array using a first value for M at a first time instance,and a second value for M at a second time instance.
 13. The method ofclaim 9, comprising setting a value for M in response to a level ofinterference detected in signals received via the antenna array.
 14. Themethod of claim 9, comprising performing, by the signal processor, aninverse wavelet transform with N bins.
 15. The method of claim 9,comprising steering antenna elements of the antenna array according tothe calculated spatial weights.
 16. The method of claim 9, comprisingfiltering, by an excision component, a result of the anti-jamming orinterference mitigation processing, at a resolution matched to the Nbins.
 17. A method for interference mitigation, comprising: receivingsignal information via an antenna array; performing, by a signalprocessor, a fast Fourier transform (FFT) with N transform domain bins,on the signal information; determining, by the signal processor, Ncovariance matrices each corresponding to a respective one of the Ntransform domain bins; grouping covariance matrices from the Ncovariance matrices into groups of M covariance matrices, each groupcorresponding to a respective group of M adjacent transform domain binsfrom the N transform domain bins; producing, by the signal processor, acombined spatial covariance matrix for each group of M covariancematrices, by combining covariance matrices within the respective groupof M covariance matrices; and calculating, by the signal processor,spatial weights from each of the spatial covariance matrices, foranti-jamming or interference mitigation processing, to control theantenna array.
 18. The method of claim 17, comprising: receiving, by theantenna array, the signal information in a radio frequency (RF) signal;converting, by an analog to digital converter (ADC), an analog signalfrom RF circuitry coupled to the antenna array, into a digital signal;and performing, by the signal processor, the FFT on the digital signal.19. The method of claim 17, comprising receiving, by the antenna array,the signal information in a global navigation satellite system (GNSS)signal.
 20. The method of claim 17, comprising controlling, by thesignal processor, the antenna array using a first value for M at a firsttime instance, and a second value for M at a second time instance.